Posts from machine learning
It is a mobile SDK that any developer can put into their mobile app and it will allow for machine learning on the device:
Machine learning (the process by which computers can get smarter through data examples instead of explicit programming) requires massive computational power, the kind usually found in clusters of computer servers in massive datacenters (ooooh, the cloud). This means that machine learning technology is usually only available to those who can connect to the cloud.
Not anymore! Clarifai’s Mobile SDK gives users the power to train and use AI in the palms of their hands by installing machine learning capability directly on their devices, bypassing the traditional requirement of internet connectivity and massive computing power. After all, these days we have tiny supercomputers in our pockets – our mobile phones. Starting with an iOS SDK, Clarifai is on a mission to make user experiences uniquely personalized on any device from your cellphone to your toaster, anywhere in the world.
Here’s a slideshow that explains how this works:
Back in February, I posted about Numeraire.
the Numerai team has now gone a step further and issued a crypto-token called Numeraire to incent these data scientists to work together to build the best models instead of just competing with each other
And roughy four months later, I am happy to write that the Numeraire token is live on the Ethereum blockchain.
You can read more about this here.
Well done Numerai team.
One of my favorite uses of AI is to use the data in your product to make your product better. I am talking about making a better UI using AI on your data.
They used the ~150mm study sets that their users have put into Quizlet over the last ~12 years to predict suggested definitions during the create study set mode.
Here is what it looks like.
I think this is super cool and a great way to make your product better.
I saw Gary Marcus give a talk at the NYU AI Event I blogged about this past week. In that
In that talk, he suggested that Deep Learning wasn’t going to get us all the way to where we want to get in AI.
I thought it was an interesting take on AI, particularly right now when the buzz and hype is so high.
This TedX talk makes the same argument and so I am sharing it with you.
NYC is an emerging hub for AI and AI startups. That is because of the large number of mathematicians, scientists, and programmers trained in AI who work on wall street, because of leading institutes like NYU’s Courant School that work on cutting-edge science in the field, and because of a number of programs aimed at AI startups here in NYC.
A few weeks ago I was at the Future Labs AI Summit to hear about AI from Yann LeCun, Gary Marcus, and many others. Below is a short highlight video of the summit.
Here is a short highlight video of the summit.
The Future Labs at NYU Tandon are now accepting applications for the second cohort or the AI NexusLab to find AI companies to support.
Applications for the next AI NexusLab cohort close Wednesday, May 3rd and conclude with the next Future Labs AI Summit in November.
If you are and AI startup or you are familiar with any early stage artificial intelligence startups who you think could benefit from our program, please have them apply at www.nexuslab.ai/
Accepted companies receive
• $100K in funding
• An NYU student fellow for the duration of the program
• mentorship from leading AI faculty and industry experts
• Access to papers and academic research
• Access to data sets
• Partner opportunity to pilot partners (the last cohort included Daimler, Tough Mudder, Quontopian, and others)
• More than 400K worth of support and services.
• Present at the next Future Labs AI Summit (last speakers included Yann LeCun, Gary Marcus, and others)
The Future Labs are also hosting office hours this Friday, April 28th from 1:00pm-5:30pm for teams who have questions about the program at the Data Future Lab – 137 Varick Street, 2nd Floor.
So you are hearing a lot about machine learning these days.
You are hearing words like models, training, forks, splits, branches, leafs, recursion, test data, and overfitting, and you don’t know what any of them mean.
The WSJ reported yesterday that Elon Musk is developing yet another company, this one based on neural lace technology, to create a brain computer interface.
Neural lace technology, as I understand it, involves implanting electrodes into the brain so that the brain can control machines directly without the need for an IO device like a mouse, keyboard, or voice interface.
I have no idea how advanced this technology is and whether it is ready for commercialization or if this is basically a research project masquerading as a startup.
But in some ways that doesn’t matter if you believe that at some point someone or some group of scientists and medical professionals will figure out how to directly connect our brain to machines without the need of an IO device.
There are so many times that I have thoughts that I don’t do anything with. They sit idle and maybe go nowhere. But if my brain passively passed those thoughts onto a machine for storage or some other action that could lead to a more productive train of thought that could be incredibly valuable. Or it could drive me insane.
I generally subscribe to the theory that all progress is good as long as we understand the negatives of the technology and we (society) engineer controls and the proper repoanes to it (nuclear weapons being an example).
But every time something as mind bending as the idea of connecting our brain to external processing, storage, and communication infrastructure comes before me I do have to pause and ask where this is all going.
At times like this it helps to have a belief system (progress is good). I am all for pushing the envelope of progress as long as we spend an equal amount of time and energy thinking through what might go wrong with things like this.
Hat tip to Niv Dror who read yesterday that I wasn’t sure how I was going to post today and encouraged me to write about this topic.
CARA is a research assistant for lawyers that offers a super simple proposition:
Securely upload a brief and discover useful case law
CARA uses Casetext’s wikipedia-like database of >10mm court cases and annotations and sophisticated natural language analysis and artificial intelligence to understand the brief and recommend related cases for a lawyer to analyze and possibly cite in their brief.
Lawyers seem to love CARA. According to Silicon Valley Business Journal:
“CARA is an invaluable, innovative research tool,” Quinn Emanuel partner David Eiseman said in a statement. “With CARA, we can upload a brief and within seconds receive additional case law suggestions and relevant information on how cases have been used in the past, all in a user-friendly interface.”
We think the legal business is ripe for AI-driven innovation. Much of legal research can and will be automated with tools like CARA.
If you are a lawyer and do a lot of legal research, check out CARA. Securely upload a brief here and check it out.